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Artificial Bee Colony Algorithm with Adaptive Explorations and Exploitations: A Novel Approach for Continuous Optimization

Mohammad Shafiul Alam, Md. Monirul Islam, Kazuyuki Murase Published in Artificial Intelligence

International Journal of Applied Information Systems
Year of Publication: 2015
© 2013 by IJAIS Journal
10.5120/ijais15-451286
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  1. Mohammad Shafiul Alam, Md. Monirul Islam and Kazuyuki Murase. Article: Artificial Bee Colony Algorithm with Adaptive Explorations and Exploitations: A Novel Approach for Continuous Optimization. International Journal of Applied Information Systems 8(2):32-43, January 2015. BibTeX

    @article{key:article,
    	author = "Mohammad Shafiul Alam and Md. Monirul Islam and Kazuyuki Murase",
    	title = "Article: Artificial Bee Colony Algorithm with Adaptive Explorations and Exploitations: A Novel Approach for Continuous Optimization",
    	journal = "International Journal of Applied Information Systems",
    	year = 2015,
    	volume = 8,
    	number = 2,
    	pages = "32-43",
    	month = "January",
    	note = "Published by Foundation of Computer Science, New York, USA"
    }
    

Abstract

A proper balance between global explorations and local exploitations is often considered necessary for complex, high dimensional optimization problems to avoid local optima and to find a good near optimum solution with sufficient convergence speed. This paper introduces Artificial Bee Colony algorithm with Adaptive eXplorations and eXploitations (ABC-AX2), a novel algorithm that improves over the basic Artificial Bee Colony (ABC) algorithm. ABC AX2 augments each candidate solution with three control parameters that control the perturbation rate, magnitude of perturbations and proportion of explorative and exploitative perturbations. Together, all the control parameters try to adapt the degree of global explorations and local exploitations around each candidate solution by affecting how new trial solutions are produced from the existing ones. The control parameters are automatically adapted at the individual solution level, separately for each candidate solution. ABC AX2 is tested on a number of benchmark problems of continuous optimization and compared with the basic ABC algorithm and several other recent variants of ABC algorithm. Results show that the performance of ABC AX2 is often better than most other algorithms in comparison, in terms of both convergence speed and final solution quality.

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Keywords

Artificial bee colony algorithm; Exploration and exploitation; Continuous optimization; Meta-heuristic optimization.